class_docstr: This class extends PyTorch's Dataloader
code: def __init__(
      self,
      dataset: BaseDataset,
      batch_size: int,
      batch_sampler: Union[Sampler],
      num_workers: Optional[int] = 1,
      pin_memory: Optional[bool] = False,
      persistent_workers: Optional[bool] = False,
      collate_fn: Optional = None,
      prefetch_factor: Optional[int] = 2,
      *args,
      **kwargs,
  ):
      super(CVNetsDataLoader, self).__init__(
          dataset=dataset,
          batch_size=batch_size,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          pin_memory=pin_memory,
          persistent_workers=persistent_workers,
          collate_fn=collate_fn,
          prefetch_factor=prefetch_factor,
          *args,
          **kwargs,
      )
  
file_path: ml-cvnets/data/loader/dataloader.py
imports: from typing import List, Optional, Union
  from torch.utils.data import DataLoader
  from data.datasets.dataset_base import BaseDataset
  from data.sampler import Sampler
label_desc: Use image node_id 1 to create a new CVNetsDataLoader object from inherited base classes: DataLoader with example: obj = CVNetsDataLoader(dataset, batch_size, batch_sampler, num_workers, pin_memory, persistent_workers, collate_fn, prefetch_factor)